Pages

Thursday, March 30, 2017

Austin Clemens — The once and future measurement of economic inequality in the United States

A slew of research into economic inequality replete with seriouslookinggraphs may give the impression that measuring inequality in the United States is a solved problem. This is misleading. Inequality is still measured incompletely because existing U.S. government statistics do not attempt to match their estimates to the National Income and Product Accounts. NIPA is the source of the most reported and well-understood economic statistics such as the nation’s Gross Domestic Product and quarterly GDP growth figures.

Because existing estimates of economic inequality are not pegged to NIPA, they don’t account for all sources of income. They may exclude, for example, fringe benefits provided by employers such as employer-provided health insurance and retirement benefits, government transfers such as supplemental nutrition assistance or the child tax credit, government services such as public education, and tax expenditures such as the home mortgage tax deduction and tax breaks for employer-provided insurance. These exclusions, big and small, make many existing estimates of inequality fundamentally incomparable to our most well-established measures of economic growth....

Important analysis from the POV of stock-flow consistency follows. Efforts are underway to improve measurement to bring estimates of income in line with national income accounting in order to remove the inconsistencies arrive at a better understanding of income and wealth distribution in the US.

The ability to look at the geographic distribution of inequality and at slices of income within different income groups teases the possibilities of a more robust project to disaggregate the National Income and Product Accounts statistics that are currently the most referenced statistics of economic progress in the nation. Devoting federal resources to the project could allow us to track inequality not only by income bands, but also by age, geographic location, gender, ethnicity, and type of income.